Using a Recurrent Neural Network To Inform the Use of Prostate-specific Antigen (PSA) and PSA Density for Dynamic Monitoring of the Risk of Prostate Cancer Progression on Active Surveillance

The global uptake of prostate cancer (PCa) active surveillance (AS) is steadily increasing. While prostate-specific antigen density (PSAD) is an important baseline predictor of PCa progression on AS, there is a scarcity of recommendations on its use in follow-up. In particular, the best way of measuring PSAD is unclear. One approach would be to use the baseline gland volume (BGV) as a denominator in all calculations throughout AS (nonadaptive PSAD, PSADNA), while another would be to remeasure gland volume at each new magnetic resonance imaging scan (adaptive PSAD, PSADA). In addition, little is known about the predictive value of serial PSAD in comparison to PSA. We applied a long short-term memory recurrent neural network to an AS cohort of 332 patients and found that serial PSADNA significantly outperformed both PSADA and PSA for follow-up prediction of PCa progression because of its high sensitivity. Importantly, while PSADNA was superior in patients with smaller glands (BGV ≤55 ml), serial PSA was better in men with larger prostates of >55 ml. Patient summary Repeat measurements of prostate-specific antigen (PSA) and PSA density (PSAD) are the mainstay of active surveillance in prostate cancer. Our study suggests that in patients with a prostate gland of 55 ml or smaller, PSAD measurements are a better predictor of tumour progression, whereas men with a larger gland may benefit more from PSA monitoring.

histological ground truth, others argue for a more personalised approach in which the need for biopsy is guided by multiparametric magnetic resonance imaging (MRI) and prostate-specific antigen (PSA) kinetics [3]. While the latter strategy could indeed improve patient adherence to AS without compromising oncological outcomes, it requires the development of robust, dynamic, risk-adapted predictive models using high-quality multi-institutional data.
Although highlighted as the current highest AS research priority [2], clinical translation of such models will require considerable time and resources. In parallel, application of longitudinal predictive modelling methods to existing MRI-driven AS cohorts can offer clinical insights that can shape future translational efforts.
We have encountered several clinical questions in our practice. First, while PSA density (PSAD) is an important baseline predictor of PCa progression on AS [4,5], there is a scarcity of recommendations on its use during follow-up [2], and specifically on the best way of measuring MRIderived PSAD. One approach would be to use baseline gland volume (BGV) as the denominator in all calculations throughout AS (nonadaptive PSAD, PSAD NA ), while another would be to remeasure gland volume whenever a new MRI scan is performed (adaptive PSAD, PSAD A ). Intuitively, PSAD A is the preferred approach given its ability to provide more accurate values with dynamic increases in prostate volume in patients on AS [6]. However, PSAD NA is easier to implement in routine clinical practice and there is no evidence regarding its comparative performance to either serial PSAD A or PSA alone. In addition, the predictive performance of longitudinal PSA, PSAD A , or PSAD NA may vary for different BGVs. Specifically, in patients with smaller prostates, even a modest increase in volume may lead to a considerable decrease in PSAD, while this effect would be the opposite in men with larger glands. In this study we tested these hypotheses using machine learning for longitudinal predictive modelling of the risk of PCa progression in patients on AS using serial PSA, PSAD A , and PSAD NA .
We included 332 patients enrolled on our previously described AS programme [4] between March 2012 and August 2020 in this ethically approved, single-centre study (Health Research Authority and Health and Care Research Wales, IRAS project ID 288,185; Supplementary Fig. 1). Clinical and histopathological characteristics of the study cohort are presented in Table 1. Over median follow-up of 51 mo (interquartile range 35-75) we collected 4508 serial PSA measurements (median 12 per patient) and performed 1362 serial prostate MRI scans (median 4 per patient). BGV for PSAD NA and follow-up gland volumes for PSAD A were calculated from MRI scans according to Prostate Imaging-Reporting and Data System guidelines [7] using three-plane measurements by four consultant urogenital radiologists with 4-14 yr of prostate MRI reporting experience. A previously described [8] long short-term memory recurrent neural network with leave-one-out crossvalidation was applied to the data to generate areas under the receiver operating characteristic curve (AUCs) for predicting PCa progression on AS. Progression was noted in 80/332 patients, defined as either histopathological (biopsy-confirmed International Society of Urological Pathology grade group upgrading) or clear radiological stage progression (PRECISE [9] score of 5). Notably, repeat biopsies were performed at protocol-driven time points or were triggered earlier by a rise in PSA or suspected MRI progression [4]. AUCs were compared using DeLong's test.
Prostate volume increased over time (Fig. 1A), consistent with previous results [6]. At the cohort level, serial PSAD NA significantly outperformed both PSAD A and PSA for prediction of PCa progression (p < 0.0001 for all; Fig. 1A,B and Supplementary Table 1). To assess the impact of BGV on biomarker performance, we a priori defined three BGV cutoffs (group A, 40 ml; group B, 41-55 ml; group C, >55 ml) to divide the cohort into three groups of similar sample size and distribution of progressors and nonprogressors (Supplementary Table 2). In groups A and B, PSAD NA showed significantly better performance in comparison to both PSAD A and PSA (p < 0.0001 for all; Fig. 1C and Supplementary Table 3). This can be explained by the higher sensitivity of PSAD NA (Fig. 1B,C), which effectively overestimates the ''true'' PSAD by maximising the impact of increasing PSA with a stable denominator of BGV. Conversely, in group C, PSA significantly outperformed both PSAD A and PSAD NA (p < 0.0001 for all; Fig. 1C and Supplementary Table 3). This probably reflects the need for a much higher relative increase in PSA to change PSAD values sufficiently to match the more rapidly increasing gland volume in patients with BGV >55 ml ( Supplementary Fig. 2). Importantly, the diagnostically superior PSAD NA and PSA are easier to use clinically given the inconsistent reporting of follow-up gland volumes as required for calculating PSAD A .
These findings can be visualised in locally weighted scatterplot smoothing curves that show more prominent differences in longitudinal trends for PSA and PSAD NA between progressors and nonprogressors in patients with smaller (55 ml) and larger (>55 ml) BGV (Fig. 1D). The same trend is evident from plots of dynamic changes in median PSA and PSAD NA values (Fig. 1F). For patients with smaller glands, PSAD NA grew steadily in progressors and plateaued in nonprogressors, while PSA showed a proportionate increase in both groups until the last year before progression/censorship. This trend was reversed for patients with larger glands: median PSA showed a much clearer relative increase in progressors in comparison to PSAD NA . Interestingly, in group A the difference between serial PSAD NA and PSAD A was considerably larger than in groups B and C, for which the two methods produced similar values (Fig. 1E).
Our study has several limitations, including its singlecentre nature, retrospective design, limited sample size, and lack of assessment of inter-reader variability for MRIderived gland volume measurement (which is generally >0.90 for expert readers [10]). While identifying specific serial PSAD NA and PSA cutoffs sufficient to trigger unscheduled MRI or biopsy was beyond the scope of this study, our data provide several observations to be tested in future work. First, regardless of BGV, a consistent increase in PSAD NA beyond the median value of 0.18 ng/ml/ml was a characteristic feature of progressors that could be first noted 3 yr before their clinical reclassification (Fig. 1A,D, Non-progressors Progressors F). Second, albeit less pronounced, a similar trend was observed for the median PSA value of 9 ng/ml; this was breached approximately 2 yr before clinical progression in patients with BGV of >55 ml (Fig. 1F).
Overall, this study offers three main observations: Dynamic monitoring of PSAD NA consistently outperformed PSAD A in predicting PCa progression on AS both for the whole AS population and in particular for patients with BGV 55 ml. Patients with BGV >55 ml benefit more from serial PSA monitoring, since PSAD is more stable and less predictive as the volume has a higher denominator value. If clinicians prefer more accurate PSAD, they should prioritise measurement of PSAD A in patients with BGV of 40 ml, for whom the discrepancy with PSAD NA is more pronounced.
These results may help in informing both current clinical practice and future multicentre studies to develop personalised AS algorithms using dynamic risk-adapted predictive modelling and incorporating all available clinical data, including serial MRI and biopsy results.